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Record W1978767201 · doi:10.1115/fedsm2014-21545

PIV Investigation of Separated and Reattached Turbulent Flows Over Ribs of Various Aspect Ratio

2014· article· en· W1978767201 on OpenAlexafffund
Kathryn Atamanchuk, Mark F. Tachie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTurbulenceTurbulence kinetic energyFreestreamAspect ratio (aeronautics)MechanicsParticle image velocimetryReynolds stressReynolds numberKinetic energyMaterials sciencePhysicsClassical mechanicsComposite material

Abstract

fetched live from OpenAlex

An experimental study is undertaken to investigate the features of separated and reattached flow over surface mounted traverse ribs of varying aspect ratio (1:1, 1:2, and 1:4) in a recirculating open channel turbulent flow. A particle image velocimetry system was used to conduct the velocity measurements. Upstream conditions were kept consistent among all three test cases. The reattachment length of the separated flow was found to decrease as rib aspect ratio increased, primarily as a result of a secondary separation reattachment formation on the ribs of increased aspect ratio. Contour plots of mean velocities, turbulence intensities, turbulent kinetic energy and Reynolds shear stresses, as well as one-dimensional profiles of streamwise mean velocity, turbulent kinetic energy and Reynolds shear stress in the recirculation and reattachment region are presented and discussed. The results show that maximum wall-normal mean velocities are approximately 40% of the approach freestream velocity. The results also indicate that the turbulence levels downstream of the block tend to decrease as the rib aspect ratio increases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.199
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2014
Admission routes2
Has abstractyes

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